This study presents an advanced sensitivity analysis for air pollution modeling using novel stochastic techniques, centered on the Unified Danish Eulerian Model (UNI-DEM) for tracking long-range air pollutant transport. This model is crucial for assessing the adverse effects of increased air pollution levels and addressing significant environmental protection issues. We propose the use of sophisticated quasi-Monte Carlo methods, employing lattice sequences for improved efficiency in multi-dimensional numerical integration and enhancing digital ecosystem modeling techniques. Our primary focus is on evaluating how variations in human-made emissions and chemical reaction rates affect the UNI-DEM model’s output. Our algorithms calculate global Sobol sensitivity indices for different input parameters, examining their impact on air pollutant levels across various European cities, acknowledging their unique geographical contexts. The goal is to deepen our understanding of the factors driving air pollution and develop effective strategies to mitigate its harmful environmental effects.